PuppyGraph raises $10 million to query enterprise data where it sits
PuppyGraph, a startup that turns enterprise data into queryable graphs without moving it, has closed a $10 million Series A round. Particle Future and defy.vc led the funding, which will help the company scale its sales and engineering teams. The startup says its AI agents can scan thousands of tables in under a second and return answers in plain English.
Editor, Lazyfounder
30 SEC SUMMARY
- PuppyGraph secured $10 million in a Series A round led by Particle Future and defy.vc, bringing total funding to $15 million.
- The startup lets AI agents query data across thousands of enterprise tables in under a second—without moving it into a separate database.
- Customers like AMD, Palo Alto Networks, and Coinbase use PuppyGraph to trace relationships across systems, such as tracking security risks from compromised accounts.
- A free version deploys via Docker in minutes, and row-level permissions are enforced at every query step.
- Annual recurring revenue grew sevenfold in the past year.
TABLE OF CONTENTS
$10M Series A extends total funding to $15 million
Particle Future and defy.vc have led PuppyGraph’s $10 million Series A round, pushing the Santa Clara startup’s total funding to $15 million. defy.vc previously led a $5 million seed round in November 2024.
Annual recurring revenue at PuppyGraph surged sevenfold over the past year, though the company has not disclosed the dollar figure.
How the engine queries data without migration
PuppyGraph’s query engine treats data sitting in warehouses and lakehouses as a single graph. This lets AI agents scan 3,000 enterprise tables and return results in under a second—without moving the data into a separate graph database.
The method sidesteps schema and context-window limits that often derail AI projects once they grow beyond small proof-of-concept datasets. Earlier this year, PuppyGraph released version 1.0, which includes an AI-assisted tool to draft schemas and lets users query in plain English.
Enterprise adoption and security
Advanced Micro Devices, Palo Alto Networks, Coinbase, Datadog, and Trend Micro already use PuppyGraph to map relationships across thousands of tables. One common use case: tracing how a compromised account could give attackers access to critical systems.
Security teams connect PuppyGraph directly to platforms like Snowflake or Databricks. The system enforces row-level permissions at every step of a query, so no data moves and no long-lived credentials are exposed.
AI integration and fast deployment
Co-founder and CEO Weimo Liu, a former Google engineer, says enterprise users now expect AI agents to answer questions that span departments—questions no one hard-coded into a dashboard. PuppyGraph’s Model Context Protocol plugs into existing AI tools like Anthropic’s Claude to turn those questions into queries.
A free version of PuppyGraph installs with a single Docker command. The company says new users can go from download to their first query in about 10 minutes.
What this means
Lazyfounder analysis — our interpretation, not reported fact.
PuppyGraph’s approach removes a key friction point for enterprises adopting AI: the need to duplicate data into a graph database. By querying warehouses and lakehouses in place, it could accelerate AI-driven analytics without disrupting existing security or compliance workflows.
Key takeaways
- Enterprises can now query sprawling datasets as a graph without migrating data, reducing complexity and cost.
- AI agents using PuppyGraph can answer cross-departmental questions in real time, beyond the scope of static dashboards.
- Row-level security is preserved, which may ease adoption in regulated industries like fintech and cybersecurity.
Related on Lazyfounder
Sources
- SiliconANGLE · 2026-11-10
PuppyGraph raises $10M to let AI agents query enterprise data as a graph
This story is an original summary drafted with AI by Lazyfounder from the reporting listed above and checked by automated validation. Facts are attributed to their original publishers; sections marked as analysis are Lazyfounder's. Where a source is in another language, facts were machine-translated and quotations are reported, not reproduced. Read the original coverage via the links, and see our AI policy and corrections policy.
About the author
Editor, Lazyfounder
Tarun Mottlia edits LazyFounders, covering Indian startups, funding rounds, AI and product launches. Every story on the site is AI-assisted and checked against its cited sources before publication.
More stories by Tarun MottliaGet the LazyFounder Brief
Startup, funding and AI news in a five-minute read. Join the early-access list.
